Adaptable vr and ar content for learning based on user&#39;s interests

ABSTRACT

A system for adapting virtual reality (VR) or augmented reality (AR) content for learning based on a user&#39;s interests includes a personalization engine that determines a user&#39;s interests and knowledge level regarding a topic, a content modifier that modifies VR or AR content related to the topic according to the users interests and knowledge level, an object selector that selects VR and AR models used to teach the topic during a VR or AR session and modifies the VR and AR models with the modified content based on the user&#39;s interests and knowledge level; a VR engine that renders the modified VR and AR models into VR or AR images, and a VR display that displays the VR or AR images. The user&#39;s engagement is measured during interaction with the VR display and used to refine and improve an understanding of the user&#39;s interest and knowledge level regarding the topic.

TECHNICAL FIELD

Embodiments of the present disclosure are directed to methods andsystems for virtual reality (VR) and augmented reality (AR).

DISCUSSION OF THE RELATED ART

Virtual reality (VR) simulates a real or imagined environment so that auser can interact with it as if physically present. It is an a immersivecomputer-generated simulation of a three-dimensional image orenvironment that can be interacted with in a seemingly real or physicalway by a person using special electronic equipment, such as a helmetwith a screen inside or gloves fitted with sensors. VR technologytypically uses virtual reality headsets to generate realistic images,sounds and other sensations that simulate a user's physical presence ina virtual or imaginary environment. A person using virtual realityequipment is able to “look around” the artificial world, move around init, and interact with virtual features or items. Augmented reality (AR)is a live view of a physical, real-world environment that is merged withcomputer-generated images across multiple sensory modalities, instead ofputting the user within a complete simulation, AR layers virtualinformation over a live camera feed, into a headset, phone screen etc.The overlaid sensory information can add to the natural environment ormask the natural environment, and is spatially registered with thephysical world such that a user perceives herself as being immersed inthe real environment. Unlike virtual reality, which replaces the realworld environment with a simulated environment, augmented reality altersone's current perception of a real world environment.

When presenting a single content in VR/AR systems to different users,the content may need modification and personalization based on the usercharacteristics to engage the user and deliver the right amount ofknowledge. For example, if a 10 year old boy has a keen interest indinosaurs, to engage him more while learning about geography of theworld, VR/AR content on world geography can be modified to use adinosaur to narrate the story, provide a visual of a dinosaur that givesa guided tour of world regions, and incorporates certain dinosaur factsinto the story to ensure user is fully engaged. If previous learninghistory indicates the user might not know about mountain formation, soextra content regarding mountain formation can be added to the originalcontent when teaching about mountains of the world. In this scenario,details about plate tectonics can be omitted, as this concept mightprove too advanced for the user of this profile.

SUMMARY

Exemplary embodiments of the present disclosure are directed to systemsand methods for maintaining user profiles to determine user interest andbackground knowledge, tagging objects in the VR/AR world to indicate anallowed level of manipulation to not corrupt the knowledge, modifyingcontent by adding/removing parts of content on the fly to match theuser's knowledge level, and by replacing content objects forpersonalization to engage the user, before the content is delivered tothe user on a VR/AR display.

According to an embodiment of the disclosure, there is provided a systemfor adapting virtual reality (VR) or augmented reality (AR) content forlearning based on a user's interests. The system includes apersonalization engine that determines a user's interests and currentknowledge level regarding a topic of interest, a content modifier thatreceives the user's interests and current knowledge level from thepersonalization engine and modifies VR or AR content related to thetopic of interest according to the users interests and current knowledgelevel, an object selector that receives the user's interests and currentknowledge level from the personalization engine and modified contentfrom the content modifier and selects VR and AR models used to teach thetopic of interest during a VR or AR session and modifies the VR and VRmodels with the modified content based on the user's interests andcurrent knowledge level; a VR engine that receives modified VR and ARmodels from the object selector and renders the modified VR and ARmodels into VR or AR images, and a VR display that displays the VR or ARimages. The user's engagement is measured during interaction with the VRdisplay and fed back into the personalization engine to refine andimprove an understanding of the user's interest and current knowledgelevel regarding the topic of interest.

According to a further embodiment of the disclosure, the system includesa content store that stores VR and AR content regarding a plurality oftopics of interest and meta data about the VR and AR content, and anobject store that stores a collection of VR and AR models related to theplurality of topics of interest stored in the content store, whereinmodels are tagged to identify ontologies and understandinter-dependencies;

According to a further embodiment of the disclosure, the personalizationengine mines the user's social media and other on-line behavior todetermine the user's interests and current knowledge level, maintains auser profile with information of the user, a database of the user'sobjects of interest, and tracks the user's current knowledge level andpast learning history.

According to a further embodiment of the disclosure, the personalizationengine determines the user's interests by one or more of presenting arange of interest categories to the user for the user to select, askingquestions of varied complexity/knowledge level on a subject matter todetermine a new user's knowledge level, tracking an existing user'sprogression on learning a selected topic of interest, or receiving anevaluation from an instructor.

According to a further embodiment of the disclosure, the personalizationengine comprises an interest and knowledge level tracking module linkedto the user's profile that tracks and analyses the user's onlineactivity, categorizes the user's actions into a predefined set ofinterest categories, and utilizes the tracked user's actions tocalculate and assign knowledge levels for various topics of interestoffered in the VR/AR system.

According to a further embodiment of the disclosure, when the user'scurrent knowledge level and learning history indicates that the VR or ARcontent has topics with which the user is unfamiliar, the contentmodifier searches the content store for the unfamiliar topics to addannotations, captions or extra VR or AR sections on the unfamiliartopics to the VR or AR content.

According to a further embodiment of the disclosure, the object selectorcustomizes VR and AR models used to teach the topic of interest byretrieving one or more personalized objects from the object store,ranking the personalized objects in order of interest to the user,injecting those personalized objects which are appropriate for thecontext of the topic of interest into content of the modified VR and ARmodels, and stores the appropriate personalized objects in the objectstore along with a set of tags that describe characteristics of theappropriate personalized objects.

According to another embodiment of the disclosure, there is provided amethod for adapting virtual reality (VR) and augmented reality (AR)content for learning based on a user's interests. The method includesthe steps of determining a user's interests and current knowledge levelregarding a topic of interest, modifying VR or AR content related to thetopic of interest according to the users interests and backgroundknowledge, selecting VR and AR models used to teach the topic ofinterest during a VR or AR session and modifying the VR and AR modelswith the modified content based on the user's interests and currentknowledge level, rendering content of modified VR and AR models into VRor AR images and displaying the VR or AR images, and measuring theuser's engagement level during interaction with the displayed images andfeeding back the user's engagement level to refine and improve anunderstanding of user's interest and current knowledge level of thetopic of interest.

According to a further embodiment of the disclosure, determining auser's interests and current knowledge level regarding a topic ofinterest comprises further comprising mining the user's social media andother on-line behavior to determine the user's interests and knowledgelevel, maintaining a user profile with information of the user and adatabase of the user's objects of interest, and tracking the user'sbackground knowledge and past learning history.

According to a further embodiment of the disclosure, determining theuser's interests comprises one or more of presenting a range of interestcategories to the user for user's interest selection, asking questionsof varied complexity/knowledge level on a subject matter to determine anew user's knowledge level, tracking an existing user's progression onlearning a selected topic of interest, or receiving an evaluation froman instructor.

According to a further embodiment of the disclosure, the method includestracking and analyzing the user's online activity, categorizing theuser's actions into a predefined set of interest categories, andutilizing the tracked user's actions to calculate and assign knowledgelevels for various topics of interest offered in an VR/AR system.

According to a further embodiment of the disclosure, when the user'sbackground. knowledge and learning history indicates that the VR or ARcontent has topics with which the user is unfamiliar, searching acontent store for the unfamiliar topics to add annotations, captions orextra VR or AR sections on the unfamiliar topics to the VR or ARcontent.

According to a further embodiment of the disclosure, the method includescustomizing the VR and AR models used to teach the topic of interest byranking the personalized objects in order of interest to the user,injecting those personalized objects which are appropriate for thecontext of the topic of interest into content of the modified VR and ARmodels, and storing the appropriate personalized objects along with aset of tags that describe characteristics of the appropriatepersonalized objects.

According to another embodiment of the disclosure, there is provided acomputer program product for adapting virtual reality (VR) or augmentedreality (AR) content for learning based on a user's interests comprisinga non-transitory program storage device readable by a computer, tangiblyembodying a program of instructions executed by the computer to causethe computer to perform a method for adapting virtual reality (VR) oraugmented reality (AR) content for learning based on a user's interests.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an virtual reality (VR)/augmented reality(AR) system according to an embodiment of the disclosure.

FIG. 2 is a flowchart of a method for adapting virtual reality (VR) andaugmented reality (AR) content for learning based on a user's interests,according to an embodiment of the disclosure.

FIG. 3 is a schematic of an exemplary cloud computing node thatimplements an embodiment of the disclosure.

FIG. 4 shows an exemplary cloud computing environment according toembodiments of the disclosure.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

Exemplary embodiments of the disclosure as described herein generallyprovide systems and methods for adapting VR and AR content for learningbased on user's interests. While embodiments are susceptible to variousmodifications and alternative forms, specific is embodiments thereof areshown by way of example in the drawings and will herein be described indetail. It should be understood, however, that there is no intent tolimit the disclosure to the particular forms disclosed, but on thecontrary, the disclosure is to cover all modifications, equivalents, andalternatives falling within the spirit and scope of the disclosure.

FIG. 1 is a block diagram of an AR/VR system 100 according to anembodiment of the disclosure. A system according to an embodimentincludes a personalization engine 110, content storage 120, a contentmodifier 130, object storage 140, an object selector 150, a VR engine160 and a VR display 170.

The personalization engine 110 according to an embodiment receives auser's selection of a topic to learn and a target time for learning thetopic, determines a user's interests and preferences, and sends theuser's interests to the object selector 150 and content modifier 130,which will use the user's interests to customize VR content topersonalize the learning experience. One way this can be achieved is bymining the user's social media and other on-line behavior, such asNetflix, Twitter, etc., to determine the user's interests. Thepersonalization engine 110 also maintains a user profile withinformation such as age, sex, location, etc., of the user, which can beused to narrow down the user's interest. The personalization engine 110can also keep track of a user's background knowledge and past learninghistory.

According to embodiments, there are no limits on how a AR/VR system canreceive input selections. Input can come from any input device, smartphone, touch screen, voice command, gesture, keyboard, VR controller,even a brain-machine interface.

The personalization engine 110 can use a variety of techniques todetermine a user's interests and knowledge level of a subject matter.Some explicit techniques include presenting a range of interestcategories to the user for user's interest selection. Similarly,personalization engine can ask questions of varied complexity/knowledgelevel on a subject matter to determine a new user's knowledge level. Theknowledge level of existing users can be determined by tracking theirprogression on the course/subject. In addition, in cases where anexternal instructor is available for the course/subject, theinstructor's input/evaluation can be another source for determining auser's knowledge level.

According to further embodiments, implicit techniques can take the formof tracking user's interests from various external sources for which theuser has given access permission. This could be in the form of a modulethat is part of the personalization engine 110, such as an interest andknowledge level tracking module that tracks and analyses the user'sonline activity, such as web searches, social media posts, onlinecontent consumption, including textual, audio, video content, etc. Atracking module can categorize the user's actions into a predefined setof interest categories. Use of an interest and knowledge level trackingmodule makes it possible to track temporal changes in user's interestsand ensures that the personalization engine's determination of userinterests is up to date. An interest and knowledge level tracking moduleaccording to an embodiment can also utilize the tracked user actions tocalculate and assign knowledge levels for various subject mattersrelevant to the subject matters offered in a VR/AR system. An interestand knowledge level tracking module can be linked to each user's profileon the VR/AR system. For example, the VR/AR system can offer a VRcontent covering “geography of the world”. The interest and knowledgelevel tracking module can collect metadata on all geography relatedcontent that the user has consumed outside the VR/AR system and use itto determine the user's current knowledge level of “geography of theworld”. For example, if the user is accessing various content tagged asAustralia, geography, year 5, then the system will determine the user'scurrent knowledge level for “geography of the world” to be year 5Australia specific. This information is then used by the ContentSelector to select content appropriate for this level of understanding.

The personalization engine 110 can also maintain a database of objectsof interest. The personalization engine mines the user's online data,profile data and asks simple questions, such as “What is your favoriteanimal?”, “What is your favorite cartoon character?”, “What is yourfavorite mammal?”, to build a database of user's objects of interest.This information about a user's favorite objects can be later used bythe object selector module 150 to select objects in the VR world.

The content storage 120 according to an embodiment stores all contentand meta data about the content, and provides it to the content storage120 upon request.

The content modifier 130 according to an embodiment receives a targetcontent selection, which is content associated with the topic ofinterest to be learned, and input from the personalization engine 110and then modifies the VR/AR content received from the content storage120 according to the user's interests and background knowledge. If theuser's background knowledge and learning history indicates that thetarget content has topics which the user has not learned in the past,the content modifier 130 searches the content storage 120 for the topicto add annotations, captions or extra VR/AR sections on the unfamiliartopics to the current content. In an example according to an embodiment,the content is “geography of the world”. The content modified by thecontent modifier 130 is subsequently delivered to the object selector150 which incorporates objects customized for each users interests intothe VR/AR experience; thus creating a highly personalized experiencewhich can increase engagement levels with the user.

Unlike prior solutions, a proposed solution according to an embodimenthas layers of VR content, and is therefore more practical and lessexpensive to maintain. One layer acts as a ‘base layer’ on to whichadditional layers will be imposed, with each layer increasing the levelof personalization. In an example according to an embodiment, the baseVR/AR layer would be “geography of the world”. A personalization layerwould determine that a user has a particular interest in learning moreabout the formation of mountains, in which case an additional AR/VRlayer relating to mountain formation will be selected from the contentstorage 120 and combined with the base layer.

According to embodiments, once all appropriate layers have been combinedin this way, the VR/AR content is passed to the object selector 150which incorporates objects aligned with the users interests, thusfurther personalizing the experience.

An object storage 140 according to an embodiment is a collection ofVR/AR models that can be swapped into the current content. Models in thestorage are tagged to identify ontologies and understandinter-dependencies.

According to embodiments, AR/VR content refers to a whole of a VR/ARworld, including the objects, scene, story, flow and movement,instructions, educational content, text, audio, music, i.e., everythingthat is communicated through a VR/AR device. On the other hand, an AR/VRmodel refers to individual visible objects that are part of the VR/ARworld, that compose the previously mentioned content.

The object selector 150 according to an embodiment receives input fromthe personalization engine 110 and modified content from the contentmodifier 130 and selects and modifies the objects stored in the VR/ARobject storage 140 used during a VR/AR session. For example, if thestory requires an animal with 4 legs, then a system according to anembodiment needs to understand that using a gorilla for this part of thestory will not be appropriate, even if the user is interested ingorillas, in an example according to an embodiment, the object is thedinosaur in/narrating the story.

According to an embodiment, the object selector 150 customizes theexperience by injecting personalized objects, such as a dinosaur, intothe VR content. The object selector 150 is informed of which objects toinclude or exclude in the story by the personalization engine 110 andretrieves the relevant objects from the object storage 140. The objectselector 150 also ranks the objects in order of interest to the user.This ranking helps determine the exposure, i.e. VR time, of theseobjects within the VR content; those with higher ranking gain moreexposure and thus the VR content can become highly personalized for eachindividual user.

According to an embodiment, since the object selector 150 only includesobjects which are appropriate for the context of the content beingdelivered, the objects can be seamlessly integrated into the VR content.In an example according to an embodiment, the topic may call for anyfour legged animal to narrate the story. For a 10 year old child withkeen interests in dinosaurs, this could be an animated Brontosaurus,whereas for a 16 year old with the same interests in dinosaurs, therendering of the dinosaur would be more lifelike. In a case where theuser has interest in horses, then the dinosaur would be replaced by ahorse. Furthermore, if both users have a similar understanding of thetopic, then the message delivered in both cases is the same, it is thedelivery method which differs.

According to an embodiment, to achieve this integration, the objectstorage 140 stores the object along with a set of tags that describecharacteristics of that object. This tagging can be achieved by acombination of manual processing and machine learning.

The VR engine 160 according to an embodiment takes input from thecontent modifier 130 and the object selector 140 and renders the imageson a VR display 170.

User engagement is measured during interaction and fed back into thepersonalization engine 110 to refine and improve the understanding ofuser's interest and current knowledge level.

According to an embodiment, the VR engine 160 measures the time a userspends looking at an object or scene in the VR world. In addition, theVR engine 160 measures how fast a user reacts to introduction andexiting of objects and scenes in the VR world by measuring how long ittakes the user to notice a new object and turn towards it. The VR engine160 also measures how long the user has looked at an object beforelooking at a different object or scene and if the user has moved towardsthe object and has tried to interact with the object and for how long.In short, the VR engine 160 measure how much attention or screen timeany object/scene/topic receives from the user. This information is thenfed back into the personalization engine 110 to update the database ofthe user's objects of interest.

FIG. 2 is a flowchart of a method for adapting VR and AR content forlearning based on a user's interests, according to an embodiment of thedisclosure. Referring now to the figure, a method includes determining auser's interests and current knowledge level at step 210 by mining theuser's social media and other on-line behavior to determine the user'sinterests, maintaining a user profile with information of the user, andtracking the user's background knowledge and past learning history. Atstep 220, VR or AR content is modified according to the user's interestsand background knowledge based on input from the personalization engine,At step 230, the VR and AR models for a VR or AR session are selectedbased on input from the personalization engine, and modified with themodified content based on the user's interests and current knowledgelevel. When the user's background knowledge and learning historyindicates that the VR or AR content has topics with which the user isunfamiliar, the VR/AR engine searches a content storage for theunfamiliar topics to add annotations, captions or extra VR or ARsections on the unfamiliar topics to the VR or AR content. At step 240,content is rendered onto images and displayed in a VR/AR display. Atstep 250, the user's engagement level during interaction with the VRdisplay is measured and feeding back the user's engagement level intothe personalization engine to refine and improve an understanding ofuser's interest and current knowledge level.

System Implementations

It is to be understood that embodiments of the present disclosure can beimplemented in various forms of hardware, software, firmware, specialpurpose processes, or a combination thereof. In one embodiment, anembodiment of the present disclosure can be implemented in software asan application program tangible embodied on a computer readable programstorage device. The application program can be uploaded to, and executedby, a machine comprising any suitable architecture. Furthermore, it isunderstood in advance that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed. An automatic troubleshooting system according to anembodiment of the disclosure is also suitable for a cloudimplementation.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting for loadbalancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to a schematic of an example of a cloud computing node isshown. Cloud computing node 310 is only one example of a suitable cloudcomputing node and is not intended to suggest any limitation as to thescope of use or functionality of embodiments of the disclosure describedherein. Regardless, cloud computing node 310 is capable of beingimplemented and/or performing any of the functionality set forth hereinabove.

In cloud computing node 310 there is a computer system/server 312, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations, Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system 312 include, but are not limitedto, personal computer systems, server computer systems, thin clients,thick clients, handheld or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system/server 312 may be described in the general context ofcomputer system. executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 312 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 3, computer system/server 312 in cloud computing node310 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 312 may include, but are notlimited to, one or more processors or processing units 316, a systemmemory 328, and a bus 318 that couples various system componentsincluding system memory 328 to processor 316.

Bus 318 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 312 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 312, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 328 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 330 and/or cachememory 332. Computer system/server 312 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 334 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided,in such instances, each can be connected to bus 418 by one or more datamedia interfaces. As will be further depicted and described below,memory 328 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the disclosure.

Program/utility 340, having a set (at least one) of program modules 342,may be stored in memory 328 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 342 generally carry out the functionsand/or methodologies of embodiments of the disclosure as describedherein.

Computer system/server 312 may also communicate with one or moreexternal devices 314 such as a keyboard, a pointing device, a display324, etc.; one or more devices that enable a user to interact withcomputer system/server 312; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 312 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 322. Still yet, computer system/server 312can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 320. As depicted, network adapter 320communicates with the other components of computer system/server 312 viabus 318. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 312. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 4, illustrative cloud computing environment 40 isdepicted. As shown, cloud computing environment 40 comprises one or morecloud computing nodes 300 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 44A, desktop computer 44B, laptop computer 44C,and/or automobile computer system 44N may communicate, Nodes 300 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 40 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 44A-N shownin FIG. 4 are intended to be illustrative only and that computing nodes300 and cloud computing environment 40 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

While embodiments of the present disclosure has been described in detailwith reference to exemplary embodiments, those skilled in the art willappreciate that various modifications and substitutions can be madethereto without departing from the spirit and scope of the disclosure asset forth in the appended claims.

What is claimed is:
 1. A system for adapting virtual reality (VR) or augmented reality (AR) content for learning based on a uses interests, comprising: a personalization engine that determines a user's interests and current knowledge level. regarding a topic of interest; a content modifier that receives the user's interests and current knowledge level from the personalization engine and modifies VR or AR content related to the topic of interest according to the user's interests and current knowledge level; an object selector that receives the user's interests and current knowledge level from the personalization engine and modified content from the content modifier and selects VR and AR models used to teach the topic of interest during a VR or AR session and modifies the VR and AR models with the modified content based on the user's interests and current knowledge level; a VR engine that receives modified VR and AR models from the object selector and renders the modified VR and AR models into VR or AR images; and a VR display that displays the VR or AR images, wherein the user's engagement is measured during interaction with the VR display and fed back into the personalization engine to refine and improve an understanding of the user's interest and current knowledge level regarding the topic of interest.
 2. The system of claim 1, further comprising: a content store that stores VR and AR content regarding a plurality of topics of interest and meta data about the VR and AR content; and an object store that stores a collection of VR and AR models related to the plurality of topics of interest stored in the content store, wherein models are tagged to identify ontologies and understand inter-dependencies.
 3. The system of claim 1, wherein the personalization engine mines the user's social media and other on-line behavior to determine the user's interests and current knowledge level, maintains a user profile with information of the user, a database of the user's objects of interest, and tracks the user's current knowledge level and past learning history.
 4. The system of claim 3, wherein the personalization engine determines the user's interests by one or more of presenting a range of interest categories to the user for the user to select, asking questions of varied complexity/knowledge level on a subject matter to determine a new user's knowledge level, tracking an existing user's progression on learning a selected topic of interest, or receiving an evaluation from an instructor.
 5. The system of claim 3, wherein the personalization engine comprises an interest and knowledge level tracking module linked to the user's profile that tracks and analyses the user's online activity, categorizes the user's actions into a predefined set of interest categories, and utilizes the tracked user's actions to calculate and assign knowledge levels for various topics of interest offered in the VR/AR system.
 6. The system of claim 2, wherein when the user's current knowledge level and learning history indicates that the VR or AR content has topics with which the user is unfamiliar, the content modifier searches the content store for the unfamiliar topics to add annotations, captions or extra VR or AR sections on the unfamiliar topics to the VR or AR content.
 7. The system of claim 2, wherein the object selector customizes VR and AR models used to teach the topic of interest by retrieving one or more personalized objects from the object store, ranking the personalized objects in order of interest to the user, injecting those personalized objects which are appropriate for the context of the topic of interest into content of the modified VR and AR models, and stores the appropriate personalized objects in the object store along with a set of tags that describe characteristics of the appropriate personalized objects,
 8. A method for adapting virtual reality (VR) and augmented reality (AR) content for learning based on a user's interests, comprising the steps of: determining a user's interests and current knowledge level regarding a topic of interest; modifying VR or AR content related to the topic of interest according to the users interests and background knowledge; selecting VR and AR models used to teach the topic of interest during a VR or AR session and modifying the VR and AR models with the modified content based on the user's interests and current knowledge level; rendering content of modified VR and AR models into VR or AR images and displaying the VR or AR images; and measuring the user's engagement level during interaction with the displayed images and feeding back the user's engagement level to refine and improve an understanding of user's interest and current knowledge level of the topic of interest.
 9. The method of claim 8, wherein determining a user's interests and current knowledge level regarding a topic of interest comprises further comprising mining the user's social media and other on-line behavior to determine the user's interests and knowledge level, maintaining a user profile with information of the user and a database of the user's objects of interest, and tracking the user's background knowledge and past learning history.
 10. The method of claim 9, wherein determining the user's interests comprises one or more of presenting a range of interest categories to the user for user's interest selection, asking questions of varied complexity/knowledge level on a subject matter to determine a new user's knowledge level, tracking an existing user's progression on learning a selected topic of interest, or receiving an evaluation from an instructor.
 11. The method of claim 9, further comprising tracking and analyzing the user's online activity, categorizing the user's actions into a predefined set of interest categories, and utilizing the tracked. user's actions to calculate and assign knowledge levels for various topics of interest offered in an VR/AR system.
 12. The method of claim 8, further comprising, when the user's background knowledge and learning history indicates that the VR or AR content has topics with which the user is unfamiliar, searching a content store for the unfamiliar topics to add annotations, captions or extra VR or AR sections on the unfamiliar topics to the VR or AR content.
 13. The method of claim 8, further comprising customizing the VR and AR models used to teach the topic of interest by ranking the personalized objects in order of interest to the user, injecting those personalized objects which are appropriate for the context of the topic of interest into content of the modified VR and AR models, and storing the appropriate personalized objects along with a set of tags that describe characteristics of the appropriate personalized objects.
 14. A computer program product for adapting virtual reality (VR) or augmented reality (AR) content for learning based on a user's interests comprising a non-transitory program storage device readable by a computer, tangibly embodying a program of instructions executed by the computer to cause the computer to perform a method comprising the steps of: determining a user's interests and current knowledge level regarding a topic of interest; modifying VR or AR content related to the topic of interest according to the users interests and background knowledge; customizing VR and AR models used to teach the topic of interest by ranking the personalized objects in order of interest to the user, injecting those personalized objects which are appropriate for the context of the topic of interest into the modified VR or AR content of VR and AR models, and storing the appropriate personalized objects along with a set of tags that describe characteristics of the appropriate personalized objects; searching a content store for the unfamiliar topics to add annotations, captions or extra VR or AR sections on the unfamiliar topics to the VR or AR content, when the user's background knowledge and learning history indicates that the VR or AR content has topics with which the user is unfamiliar and rendering content of customized VR and AR models into VR or AR images and displaying the VR or AR images.
 15. The computer program product of claim 14, wherein determining a user's interests and current knowledge level regarding a topic of interest comprises further comprising mining the user's social media and other on-line behavior to determine the laser's interests and knowledge level, maintaining a user profile with information of the user and a database of the user's objects of interest, and tracking the user's background knowledge and past learning history.
 16. The computer program product of claim 15, wherein determining the user's interests comprises one or more of presenting a range of interest categories to the user for user's interest selection, asking questions of varied complexity/knowledge level on a subject matter to determine a new user's knowledge level, tracking are existing user's progression on learning a selected topic of interest, or receiving an evaluation from an instructor.
 17. The computer program product of claim 15, the method further comprising tracking and analyzing the user's online activity, categorizing the user's actions into a predefined set of interest categories, and utilizing the tracked user's actions to calculate and assign knowledge levels for various topics of interest offered in an VR/AR system.
 18. The computer program product of claim 14, the method further comprising measuring the user's engagement level during interaction with the displayed images and feeding back the user's engagement level to refine and improve an understanding of user's interest and current knowledge level of the topic of interest.
 19. The computer program product of claim 14, the method further comprising: selecting VR and AR models used to teach the topic of interest during a VR or AR session and modifying VR and AR models with the modified content based on the user's interests and current knowledge level. 